评估神经网络模型分析市场上客户评论情绪的效果

Kana Karunia, Aprilya Eka Putri, May Dila Fachriani, Muhammad Hilman Rois
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引用次数: 0

摘要

根据 2019 年的报告,Tokopedia 是访问量最大的市场,月访问量达 140,000,000 人次,是印度尼西亚最受欢迎的市场之一。客户有机会在 Tokopedia 上的交易过程结束时撰写对所购产品的评论。本研究旨在对 Tokopedia 上的产品评论进行情感分析。用于文本分类的三个神经网络是 Bi-GRU、GRU 和 LSTM。数据处理技术分为训练样本和测试样本,使用保持技术将训练样本和测试样本分成 80%:20%。BI-GRU 算法的准确率为 0.93%,精度为 0.96,优于其他两种方法 LSTM 和 GRU,前者的准确率为 0.92,召回率为 0.91。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of the Effectiveness of Neural Network Models for Analyzing Customer Review Sentiments on Marketplace
According to the 2019 report, Tokopedia is the most visited marketplace with 140,000,000 visitors per month, making it one of the most popular marketplaces in Indonesia. Customers have the opportunity to write reviews about the products they purchase at the end of the transaction process on Tokopedia. The aim of this research is to conduct sentiment analysis on product reviews on Tokopedia. Three neural networks that will be used for text classification are Bi-GRU, GRU, and LSTM. The data processing technique is divided into training and testing samples, split into 80%:20% using the holdout technique. The BI-GRU algorithm has an accuracy of 0.93% and precision of 0.96, better than the other two methods LSTM and GRU, which each have an accuracy of 0.92 and recall of 0.91.
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